Basketball Reference

Loading Data from Basketball Reference

R Package for Manipulating Data: The Tidyverse

• tidyverse

install.packages("tidyverse", repos = "http://cran.us.r-project.org")

R Package for Acquiring Data

• hoopR

if (!requireNamespace('devtools', quietly = TRUE)){
  install.packages('devtools')
}
devtools::install_github("sportsdataverse/sportsdataverse-R")

R Package for Visualizing + Analyzing Data

• BasketballAnalyzeR

devtools::install_github("sndmrc/BasketballAnalyzeR")

Graphing the Four Factors: First Attempt

Visualizing the Four Factors: Going Straight to the Source Code

Creating Shot Charts

Creating an Assist Network

Introduction to Expected Points: Tatum’s Deuce

Question: How Do Expected Points Change Based on Distance from the Basket?

(Conditional) Expected Points for Starters

Cluster Analysis

Question: How Many Possible March Madness Brackets are There?

How Brackets are Typically Scored

Round One: 1

Round Two: 2

Sweet Sixteen: 4

Elite Eight: 8

Final Four: 16

Championship: 32

A perfect bracket gets a score of 192.

Ranking Teams Using Metrics

This could be as simple as using AP rankings, or you could develop your own metric. You can evaluate your metric based on how it performs on past tournaments.

Example Metrics

Ranking Teams Using Metrics

• We can pick the best team at each stage
• Strict hierarchy is unrealistic and one prediction for a tournament has a lot of uncertainty

Predicting Individual Games

We can predict the probability of a team winning a certain March Madness game.

Question: What are Potential Variables for Our Model?

A “Probabilistic” Approach

Depending on choice of model, it may be possible that the team most likely to advance at one stage may be less likely to advance at a future stage.

A “Probabilistic” Approach

• Can account for differences in playstyles
• Not practical to compute (9,223,372,036,854,775,808 different possibilities to consider)

How Can We Model How “Good” a Team is?

We can predict how many points a team will score in a March Madness game.

Regression works for continuous variables that have a support of \((-\infty, \infty)\), so we must use a link function to map the counts to real variables.

Poisson Regression: Predicting Points Scored

For regression, let \(Y\) be the number of points scored by the team of interest, and let \(x_j\) be the \(j\)th predictor out of \(n\).

Then \(\log(E(Y | x)) = \theta_0 + \theta_1x_1 + \theta_2x_2 + ... + \theta_nx_n\)

A Simulation Approach

For each team in a game, we can draw from Pois\((\lambda)\), where \(\lambda\) is the predicted response from our regression for that team’s points. The team that scores more points advances. We can simulate a tournament as many times as we want. Then we can get an idea of how likely a team is to make it to a certain round. Note that the most likely bracket may not coincide with the most likely winner.

A Simulation Approach

Pros/Cons of Simulation

• Doesn’t require as much processing
• Hard to find optimal bracket

Vibes Bracket

Pick the teams you think will win, or the teams you personally want to win. It worked great for Florida students! Just make sure you don’t pick too few or too many upsets.

Picking Upsets

NCAA Average Number of Upsets:

• Total Upsets: 8.5
• First Round: 4.65
• Second Round: 3.13
• Elite Eight: 0.31
• Final Four: 0.10

Conclusion and Further Resources

• Basketball Reference
• Paper on “Four Factors”
• hoopR - Package for Aquiring Data
• BasketballAnalyzeR - Package for Shot Charts, Assist Networks, Bubble Plot